smart.lab.tunisia@gmail.com +216 71 588 443
Connexion

Nadia Ben Hadj Boubaker

Informations générales

Nadia Ben Hadj Boubaker
Grade

Doctorant

Biographie courte

I am a first-year PhD student specializing in recommender systems and explainability. My research focuses on leveraging deep learning models and knowledge graphs to enhance recommendation performance and transparency. I have several publications in international conferences.

Axes de recherche

Artificial Intelligence

Publications

  • 2024
    Nadia Ben Hadj Boubaker, Zahra Kodia, Nadia Yacoubi Ayadi

    Personalized E-Learning Knowledge Graph-based Recommender System using Ensemble Attention Networks

    Boubaker, N. B. H., Kodia, Z., & Ayadi, N. Y. (2024, November). Personalized E-Learning Knowledge Graph-based Recommender System using Ensemble Attention Networks. In International Conference on Management of Digital (pp. 84-100). Cham: Springer Nature Sw, 2024

    Résumé

    In a rapidly evolving digital landscape, recommender systems have become essential tools for helping users navigate overwhelming amounts of information in various domains. In e-learning contexts, these systems aim to support learners by identifying educational resources or relevant academic content that are significant for their learning experience. However, research incorporating domain knowledge, such as course-related concepts, to improve recommendation quality remains limited. This paper presents a new personalized recommender system in e-learning context, which is called KA-ERN: Knowledge-based Attention Ensemble Recurrent Network. Specifically, KA-ERN leverages a Knowledge Graph to capture the dependencies and semantic relationships between users, courses, and their related concepts and then, performs ensemble learning by combining the Bidirectional Long Short-Term Memory network (Bi-LSTM) with the Artificial Neural Network (ANN). An attention mechanism is added to enhance recommendation quality. Our approach is based on a two-stage architecture. First, entities and relations embeddings are generated and then concatenated as sequences allowing the model to capture complex relationships and contextual dependencies of user preferences. Secondly, these embeddings are provided as inputs to the KA-ERN model. The proposed combination of Bi-LSTM network, ANN, and attention mechanisms shows the advantage of using Knowledge graph embeddings over bipartite graph embeddings capturing only user-item interactions and experimental results achieving the best recommendation metrics, with RMSE of 0.045 and MAE of 0.034, outperforming baseline methods.
    Nadia Ben Hadj Boubaker, Zahra Kodia, Nadia Yacoubi Ayadi

    Personalized E-Learning Knowledge Graph-based Recommender System using Ensemble Attention Networks

    In: Chbeir, R., et al. Management of Digital EcoSystems. MEDES 2024. Communications in Computer and Information Science, vol 2518. Springer, Cham., 2024

    Résumé

    In a rapidly evolving digital landscape, recommender systems have become essential tools for helping users navigate overwhelming amounts of information in various domains. In e-learning contexts, these systems aim to support learners by identifying educational resources or relevant academic content that are significant for their learning experience. However, research incorporating domain knowledge, such as course-related concepts, to improve recommendation quality remains limited. This paper presents a new personalized recommender system in e-learning context, which is called KA-ERN: Knowledge-based Attention Ensemble Recurrent Network. Specifically, KA-ERN leverages a Knowledge Graph to capture the dependencies and semantic relationships between users, courses, and their related concepts and then, performs ensemble learning by combining the Bidirectional Long Short-Term Memory network (Bi-LSTM) with the Artificial Neural Network (ANN). An attention mechanism is added to enhance recommendation quality. Our approach is based on a two-stage architecture. First, entities and relations embeddings are generated and then concatenated as sequences allowing the model to capture complex relationships and contextual dependencies of user preferences. Secondly, these embeddings are provided as inputs to the KA-ERN model. The proposed combination of Bi-LSTM network, ANN, and attention mechanisms shows the advantage of using Knowledge graph embeddings over bipartite graph embeddings capturing only user-item interactions and experimental results achieving the best recommendation metrics, with RMSE of 0.045 and MAE of 0.034, outperforming baseline methods.